Abstract:
Electrical power industry restricting has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand of electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. This work proposes a novel metaheuristic optimization methodology aimed at solving economic dispatch problem considering valve point loading effects. The differential evolution (DE) may occasionally stop proceeding toward the global optimum even though the population has not converged to a local optimum. This situation is usually referred to as stagnation. DE also suffers from the problem of premature convergence, where the population converges to some local optima of a multimodal objective function, losing its diversity. Shuffled frog leaping algorithm (SFLA) is a newly developed mimetic metaheuristic algorithm for combinatorial optimization, which has simple concept, few parameters, high performance, and easy programming. SFLA and its variants have been successfully applied to various fields of power system optimization. The proposed approach is based on a hybrid shuffled differential evolution (SDE) algorithm which combines the benefits of SFLA and DE. The SDE algorithm integrates a novel differential mutation operator specifically designed to effectively address the problem under study. In order to validate the proposed methodology, detailed simulation results obtained on three standard test systems having 3, 13, and 40-units are presented and discussed. A comparative analysis with other settled nature-inspired solution algorithms demonstrates the superior performance of the proposed methodology in terms of both solution accuracy and convergence performances.